Cicada Flux Measurements from 2021

Two Questions are developed on how cicada emergene influences soil respiration and carbon fluxes:

1) To what extent do periodical cicada emergence holes influence soil CO2 efflux? Are responses variable across species or mycorrhizal fungal types?

2) What levels of disruption to ecosystem scale respiration can be attributed to periodical cicada emergence hole densities?

Mechanistic hypothesis:

Conceptual model of competing mechansisms influencing soil respiration following cicada emergence. Nymph cicadas form holes aerating soils (a)) increasing aerobic conditions in the soils increasing both autotrophic (Ra) and heterotropic respiration (Rh). Conversely, emergence holes also increase infiltration (b)) reducing CO2 and oxygen diffusion potential creating anaerobic conditions. Subsequent conditions result in elevated CH4 efflux.

Soil temperature and moisture among cicada holes

Figure 2: Soil temperature (a)) and moisture (b)) with respect to the number of cicada holes within the soil respiration collars. No significant differences were noted as collars were typically located within 30 cm of each other.

##  Family: gaussian 
##   Links: mu = identity; sigma = identity 
## Formula: Corr_LinFlux ~ Week + Symbiont 
##    Data: Rs_plots[Rs_plots$Corr_LinFlux <= 6 & Rs_plots$Sit (Number of observations: 214) 
##   Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
##          total post-warmup draws = 4000
## 
## Population-Level Effects: 
##                Estimate Est.Error l-94% CI u-94% CI Rhat Bulk_ESS Tail_ESS
## Intercept          1.99      0.14     1.73     2.25 1.00     2109     2827
## Week2021M06M14    -0.19      0.20    -0.57     0.19 1.00     3139     2822
## Week2021M06M21    -0.89      0.17    -1.21    -0.56 1.00     2647     2982
## Week2021M07M05    -0.36      0.18    -0.68    -0.02 1.00     2595     3102
## Week2021M07M19     0.06      0.21    -0.34     0.46 1.00     2766     3072
## Week2021M08M02    -0.18      0.20    -0.57     0.20 1.00     3164     3423
## SymbiontECM        0.11      0.12    -0.12     0.32 1.00     6397     3086
## 
## Family Specific Parameters: 
##       Estimate Est.Error l-94% CI u-94% CI Rhat Bulk_ESS Tail_ESS
## sigma     0.83      0.04     0.76     0.92 1.00     5438     2702
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
## Hypothesis Tests for class b:
##                 Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio
## 1 (Intercept)-(Inte... = 0    -0.11      0.12    -0.33     0.13         NA
##   Post.Prob Star
## 1        NA     
## ---
## 'CI': 90%-CI for one-sided and 95%-CI for two-sided hypotheses.
## '*': For one-sided hypotheses, the posterior probability exceeds 95%;
## for two-sided hypotheses, the value tested against lies outside the 95%-CI.
## Posterior probabilities of point hypotheses assume equal prior probabilities.
##  Family: gaussian 
##   Links: mu = identity; sigma = identity 
## Formula: Corr_LinFlux ~ n_holes * Symbiont + (1 | Site) 
##    Data: Rs_plots[Rs_plots$Corr_LinFlux <= 6 & Rs_plots$Sit (Number of observations: 214) 
##   Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
##          total post-warmup draws = 4000
## 
## Group-Level Effects: 
## ~Site (Number of levels: 2) 
##               Estimate Est.Error l-94% CI u-94% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     0.75      0.64     0.07     2.35 1.00      814     1313
## 
## Population-Level Effects: 
##                     Estimate Est.Error l-94% CI u-94% CI Rhat Bulk_ESS Tail_ESS
## Intercept               1.34      0.50     0.23     2.33 1.01     1058     1074
## n_holes                 0.64      0.14     0.38     0.91 1.00     1811     1854
## SymbiontECM             0.35      0.14     0.08     0.62 1.00     2119     2400
## n_holes:SymbiontECM    -0.57      0.24    -1.02    -0.12 1.00     1872     2345
## 
## Family Specific Parameters: 
##       Estimate Est.Error l-94% CI u-94% CI Rhat Bulk_ESS Tail_ESS
## sigma     0.85      0.04     0.77     0.93 1.00     2402     2097
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
## Hypothesis Tests for class b:
##                 Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio
## 1 (Intercept)-(Inte... = 0    -0.35      0.14    -0.63    -0.07         NA
##   Post.Prob Star
## 1        NA    *
## ---
## 'CI': 90%-CI for one-sided and 95%-CI for two-sided hypotheses.
## '*': For one-sided hypotheses, the posterior probability exceeds 95%;
## for two-sided hypotheses, the value tested against lies outside the 95%-CI.
## Posterior probabilities of point hypotheses assume equal prior probabilities.

Figure 3: Soil respiration for cicada holes (a)) and seasonal trends (b)) with respect to the fungal type

##  Family: gaussian 
##   Links: mu = identity; sigma = log 
## Formula: Corr_LinFlux ~ asym * exp(scale * SoilT_C) 
##          scale ~ 1
##          asym ~ 1
##          sigma ~ 1
##    Data: Rs_plots_sum[Rs_plots_sum$Hole_Collar == 0, ] (Number of observations: 18) 
##   Draws: 4 chains, each with iter = 8000; warmup = 4000; thin = 1;
##          total post-warmup draws = 16000
## 
## Population-Level Effects: 
##                 Estimate Est.Error l-94% CI u-94% CI Rhat Bulk_ESS Tail_ESS
## sigma_Intercept    -0.75      0.18    -1.06    -0.39 1.00     3801     4156
## scale_Intercept     0.05      0.03    -0.01     0.11 1.01     1480     1098
## asym_Intercept      0.68      0.44     0.17     1.75 1.01     1486     1097
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
##  Family: gaussian 
##   Links: mu = identity; sigma = log 
## Formula: Corr_LinFlux ~ asym * exp(scale * SoilT_C) 
##          scale ~ 1
##          asym ~ 1
##          sigma ~ 1
##    Data: Rs_plots_sum[Rs_plots_sum$Hole_Collar == 1, ] (Number of observations: 18) 
##   Draws: 4 chains, each with iter = 80000; warmup = 40000; thin = 1;
##          total post-warmup draws = 160000
## 
## Population-Level Effects: 
##                 Estimate Est.Error l-94% CI u-94% CI Rhat Bulk_ESS Tail_ESS
## sigma_Intercept    -0.77      0.18    -1.09    -0.40 1.00     3971     2396
## scale_Intercept     0.09      0.04     0.02     0.17 1.00      721      230
## asym_Intercept      0.38      0.31     0.07     1.16 1.00      728      238
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
##  Family: gaussian 
##   Links: mu = identity; sigma = identity 
## Formula: Q10 ~ Treatment 
##    Data: Q10_data (Number of observations: 2000) 
##   Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
##          total post-warmup draws = 4000
## 
## Population-Level Effects: 
##                  Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept            2.72      0.03     2.67     2.77 1.00     3843     2696
## TreatmentNoHoles    -0.96      0.04    -1.03    -0.89 1.00     3607     2787
## 
## Family Specific Parameters: 
##       Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma     0.83      0.01     0.81     0.86 1.00     3802     3128
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
## Hypothesis Tests for class b:
##                 Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio
## 1 (Intercept)-(Inte... = 0     0.96      0.04     0.89     1.03         NA
##   Post.Prob Star
## 1        NA    *
## ---
## 'CI': 90%-CI for one-sided and 95%-CI for two-sided hypotheses.
## '*': For one-sided hypotheses, the posterior probability exceeds 95%;
## for two-sided hypotheses, the value tested against lies outside the 95%-CI.
## Posterior probabilities of point hypotheses assume equal prior probabilities.

Figure 4: The soil temperature effects soil respiration (a)) for collars containing cicada holes (Blue) and no cicada holes (burgundy). Collars containing cicada holes are more sensitive to soil temperature (b)) as soil Q10 was significantly different between the two treatments.

Spatially scaling of soil respiration and cicada emergence

Region of interest:

Theortical footprint for cicada respirtation effects at Morgan-Monroe State Forest AmeriFlux site. Buffer represents a 20 m buffer (red polygon) surrounding each pair of soil respiration collars (points).

Mixed effects model for spatial interpolation

##  Family: gaussian 
##   Links: mu = identity; sigma = identity 
## Formula: Corr_LinFlux ~ SoilT_C + SoilVWC_pct + n_holes * Symbiont + (1 | Site) 
##    Data: Rs_plots[Rs_plots$Corr_LinFlux <= 6 & Rs_plots$Sit (Number of observations: 209) 
##   Draws: 4 chains, each with iter = 40000; warmup = 20000; thin = 1;
##          total post-warmup draws = 80000
## 
## Group-Level Effects: 
## ~Site (Number of levels: 2) 
##               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     1.07      1.03     0.14     3.85 1.00     3212     1413
## 
## Population-Level Effects: 
##                     Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept              -3.10      1.07    -5.25    -0.98 1.00    14632     7739
## SoilT_C                 0.24      0.04     0.16     0.31 1.00    52149    34977
## SoilVWC_pct            -0.00      0.01    -0.02     0.01 1.00    41459    34153
## n_holes                 0.64      0.13     0.39     0.89 1.00    31029    38162
## SymbiontECM             0.34      0.13     0.09     0.59 1.00    37929    46911
## n_holes:SymbiontECM    -0.53      0.22    -0.96    -0.11 1.00    24610    34126
## 
## Family Specific Parameters: 
##       Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma     0.78      0.04     0.71     0.86 1.00    43287    36966
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Model selection output Model: Corr_LinFlux ~ SoilT_C + SoilVWC_pct + n_holes * Symbiont + (1|Site) ### elpd_diff se_diff #### MEM_Mod2 0.0 0.0
#### MEM_Mod1 -0.1 1.5
#### MEM_Mod3 -2.5 2.3

## [1] 0.1959534
## [1] 0.05834767
## OGR data source with driver: ESRI Shapefile 
## Source: "D:\Dropbox\Projects\Indiana\Data\CicadaFlux\GIS\MMSF_Cicada_Pheno", layer: "PhenoHoles"
## with 120 features
## It has 10 fields
## Integer64 fields read as strings:  field_1 Holes_mx Holes_md_m
## OGR data source with driver: ESRI Shapefile 
## Source: "D:\Dropbox\Projects\Indiana\Data\CicadaFlux\GIS\MMSF_Cicada_Pheno", layer: "PhenoHoles_Dissolve_15"
## with 1 features
## It has 10 fields
## Integer64 fields read as strings:  field_1 Holes_mx Holes_md_m

## [inverse distance weighted interpolation]

## [inverse distance weighted interpolation]

## [inverse distance weighted interpolation]

##  [1] "day"              "UnqMeas"          "Type"             "Etime"           
##  [5] "Tcham"            "Pressure"         "H2O"              "CO2"             
##  [9] "Cdry"             "Tbench"           "RH"               "Tboard"          
## [13] "Vin"              "CO2ABS"           "H2OABS"           "Hour"            
## [17] "DOY"              "RAWCO2"           "RAWCO2REF"        "RAWH2O"          
## [21] "RAWH2OREF"        "OBS"              "VCham"            "Offset"          
## [25] "Area"             "VTotal"           "ExpFlux"          "ExpFluxCV"       
## [29] "Exp_dCdt"         "ExpR2"            "LinFlux"          "LinFluxCV"       
## [33] "Lin_dCdt"         "LinR2"            "LinFluxSE"        "File"            
## [37] "Collar"           "LinReg_R2"        "CO2_t0"           "CO2_t0_LCI"      
## [41] "CO2_t0_HCI"       "dCdt"             "dCdt_LCI"         "dCdt_HCI"        
## [45] "Site"             "Date"             "SoilT_C"          "SoilVWC_pct"     
## [49] "n_holes"          "Corr_offset_cm"   "Pair"             "Species"         
## [53] "Symbiont"         "Hole_Collar"      "Hole_Plot_m2"     "Latitude"        
## [57] "Longitude"        "Altitude"         "VCollar"          "Corr_VTotal"     
## [61] "Corr_LinFlux"     "Corr_LinFlux_LCI" "Corr_LinFlux_HCI" "Week"            
## [65] "n_holes_f"

##  [1] "TIMESTAMP_START"     "TIMESTAMP_END"       "USTAR_1_1_1"        
##  [4] "TA_1_1_1"            "WD_1_1_1"            "WS_1_1_1"           
##  [7] "FC_1_1_1"            "H_1_1_1"             "LE_1_1_1"           
## [10] "G_2_1_1"             "TS_2_1_1"            "P_1_1_1"            
## [13] "RH_1_1_1"            "PA_1_1_1"            "CO2_1_1_1"          
## [16] "VPD_PI_1_1_1"        "SWC_PI_1"            "NETRAD_1_1_1"       
## [19] "PPFD_IN_1_1_1"       "SW_IN_1_1_1"         "SW_OUT_1_1_1"       
## [22] "LW_IN_1_1_1"         "LW_OUT_1_1_1"        "H2O_1_1_1"          
## [25] "RECO_PI_1_1_1"       "PPFD_DIF_1_1_1"      "T_SONIC_1_2_1"      
## [28] "TA_1_2_1"            "TA_1_3_1"            "RH_1_2_1"           
## [31] "RH_1_3_1"            "CO2_1_2_1"           "H2O_1_2_1"          
## [34] "SW_IN_1_2_1"         "SW_OUT_1_2_1"        "LW_IN_1_2_1"        
## [37] "LW_OUT_1_2_1"        "U_SIGMA_1_2_1"       "V_SIGMA_1_2_1"      
## [40] "W_SIGMA_1_2_1"       "T_SONIC_SIGMA_1_2_1" "T_SONIC_1_1_1"      
## [43] "T_SONIC_SIGMA_1_1_1" "PPFD_IN_1_1_2"       "TA_1_1_2"           
## [46] "RH_1_1_2"            "WS_1_1_2"            "WD_1_1_2"           
## [49] "U_SIGMA_1_1_1"       "V_SIGMA_1_1_1"       "W_SIGMA_1_1_1"      
## [52] "T_SONIC_2_1_1"       "CO2_2_1_1"           "H2O_2_1_1"          
## [55] "SW_BC_IN_1_1_1"      "SW_BC_OUT_1_1_1"     "LW_BC_IN_1_1_1"     
## [58] "LW_BC_OUT_1_1_1"     "PPFD_BC_IN_1_1_1"    "TA_2_1_1"           
## [61] "G_2_1_2"             "P_2_1_1"             "U_SIGMA_2_1_1"      
## [64] "V_SIGMA_2_1_1"       "W_SIGMA_2_1_1"       "T_SONIC_SIGMA_2_1_1"
## [67] "WS_1_2_1"            "WD_1_2_1"            "WS_2_1_1"           
## [70] "WD_2_1_1"            "SWC_1_1_1"           "SWC_2_1_1"          
## [73] "SWC_3_1_1"           "SWC_4_1_1"           "SWC_5_1_1"          
## [76] "SWC_6_1_1"           "SWC_1_PI_SD"

## 
## Call:
## lm(formula = comp_data$Flux ~ comp_data$Treatment)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5539 -1.5020 -0.2426  1.3615  8.3191 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                    2.6120     0.1560  16.748   <2e-16 ***
## comp_data$TreatmentPreCicada   0.2925     0.2206   1.326    0.186    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.758 on 252 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.006932,   Adjusted R-squared:  0.002991 
## F-statistic: 1.759 on 1 and 252 DF,  p-value: 0.1859
## 
## Call:
## lm(formula = comp_data$SoilT ~ comp_data$Treatment)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -11.4249  -5.0518  -0.2734   5.7392   9.5706 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                  12.52795    0.52333  23.939   <2e-16 ***
## comp_data$TreatmentPreCicada  0.01079    0.74010   0.015    0.988    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.898 on 252 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  8.433e-07,  Adjusted R-squared:  -0.003967 
## F-statistic: 0.0002125 on 1 and 252 DF,  p-value: 0.9884

##  Family: lognormal 
##   Links: mu = identity; sigma = log 
## Formula: FC_Md ~ asym * exp(scale * ST_Md) 
##          scale ~ 1
##          asym ~ 1
##          sigma ~ 1
##    Data: PreCicada_sumz (Number of observations: 366) 
##   Draws: 3 chains, each with iter = 10000; warmup = 5000; thin = 1;
##          total post-warmup draws = 15000
## 
## Population-Level Effects: 
##                 Estimate Est.Error l-89% CI u-89% CI Rhat Bulk_ESS Tail_ESS
## sigma_Intercept    -1.02      0.04    -1.08    -0.96 1.00     5292     5108
## scale_Intercept     0.11      0.00     0.10     0.12 1.00     4671     5380
## asym_Intercept      0.16      0.01     0.14     0.18 1.00     4567     5225
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
##     Estimate   Est.Error      Q5.5     Q89
## R2 0.6941538 0.009433892 0.6776104 0.70492

##  Family: lognormal 
##   Links: mu = identity; sigma = log 
## Formula: FC_Md ~ asym * exp(scale * ST_Md) 
##          scale ~ 1
##          asym ~ 1
##          sigma ~ 1
##    Data: Cicada_sumz[Cicada_sumz$FC_Md > 0, ] (Number of observations: 305) 
##   Draws: 3 chains, each with iter = 10000; warmup = 5000; thin = 1;
##          total post-warmup draws = 15000
## 
## Population-Level Effects: 
##                 Estimate Est.Error l-89% CI u-89% CI Rhat Bulk_ESS Tail_ESS
## sigma_Intercept    -0.62      0.04    -0.68    -0.55 1.00     5782     5261
## scale_Intercept     0.12      0.01     0.11     0.13 1.00     4881     6085
## asym_Intercept      0.12      0.02     0.09     0.15 1.00     4847     5882
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
##     Estimate  Est.Error      Q5.5       Q89
## R2 0.6019341 0.02491527 0.5582458 0.6299587

##  Family: gaussian 
##   Links: mu = identity; sigma = identity 
## Formula: Q10 ~ Treatment 
##    Data: EC_Q10_d (Number of observations: 30000) 
##   Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
##          total post-warmup draws = 4000
## 
## Population-Level Effects: 
##                     Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept               3.00      0.00     3.00     3.01 1.00     3477     2951
## TreatmentCicadaYear     0.38      0.00     0.38     0.39 1.00     2690     2495
## 
## Family Specific Parameters: 
##       Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma     0.22      0.00     0.22     0.22 1.00     1922     1922
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
## Hypothesis Tests for class b:
##                 Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio
## 1 (Intercept)-(Inte... = 0    -0.38         0    -0.39    -0.38         NA
##   Post.Prob Star
## 1        NA    *
## ---
## 'CI': 90%-CI for one-sided and 95%-CI for two-sided hypotheses.
## '*': For one-sided hypotheses, the posterior probability exceeds 95%;
## for two-sided hypotheses, the value tested against lies outside the 95%-CI.
## Posterior probabilities of point hypotheses assume equal prior probabilities.